PubChem BioAssay: 2017 update

By Yanli Wang, Stephen H. Bryant, Tiejun Cheng, Jiyao Wang, Asta Gindulyte, Benjamin A. Shoemaker, Paul A. Thiessen, Siqian He, Jian Zhang. Nucleic Acids Res., 2016

590 citations recorded by the source corpus.

PubChem's BioAssay database (https://pubchem.ncbi.nlm.nih.gov) has served as a public repository for small-molecule and RNAi screening data since 2004 providing open access of its data content to the community. PubChem accepts data submission from worldwide researchers at academia, industry and government agencies. PubChem also collaborates with other chemical biology database stakeholders with data exchange. With over a decade's development effort, it becomes an important information resource supporting drug discovery and chemical biology research. To facilitate data discovery, PubChem is integrated with all other databases at NCBI. In this work, we provide an update for the PubChem BioAssay database describing several recent development including added sources of research data, redesigned BioAssay record page, new BioAssay classification browser and new features in the Upload system facilitating data sharing.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d82dfffdedbad1e44cf82374221604a69e58fa97

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Citations in the corpus

Differentiable Scaffolding Tree for Molecular Optimization (2021, unknown): “Examples include using biological assays to determine the potency of drug candidates [44], or conducting electronic structure calculation to determine photoelectric properties [33].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “With the optimized model, we predicted DTIs from bioassays such as PubChem BioAssays and KinaseSARfari to estimate the performance of our model.”
Fréchet ChemblNet Distance: A metric for generative models for molecules (2018, methodology): “vations of the penultimate layer of ChemNet. The model [15] was trained to predict bioactivities of about 6000 assays available in three major drug discovery databases (ChEMBL [2], ZINC [11], PubChem [24]). We used long short-term memory (LSTM) [10] recurrent neural networks based on the one-hot enc”
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery (2018, methodology): “The model [15] was trained to predict bioactivities of about 6 000 assays available in three major drug discovery databases (ChEMBL [2], ZINC [11], PubChem [24]).”
A Deep Learning Approach to Antibiotic Discovery. (2020, background): “Nevertheless, given the increasing volume of screening data that exists (Wang et al., 2017), carefully leveraging these resources could result in millions of molecular graph-biological property relationships, provided that the data are of adequate quality and methodological uniformity so that…”
Database resources of the National Center for Biotechnology Information (2017, background): “PubChem (9,10) is a resource that provides information on various chemical entities, including small molecules, siRNAs, miRNAs, carbohydrates, lipids, peptides, chemically modified macromolecules, and many others.”